Application of long term and short term memory neural network in prediction of chlorophyll <i>a</i> concentration

Shi Suixiang; Wang lei; Yu Xuan; Xu Lingyu

doi:10.3969/j.issn.0253-4193.2020.02.014

Volume 42 Issue 2

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Shi Suixiang，Wang lei，Yu Xuan, et al. Application of long term and short term memory neural network in prediction of chlorophyll a concentration[J]. Haiyang Xuebao，2020, 42(2)：134–142，doi:10.3969/j.issn.0253−4193.2020.02.014

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Application of long term and short term memory neural network in prediction of chlorophyll a concentration

doi: 10.3969/j.issn.0253-4193.2020.02.014

Shi Suixiang^1
,,
Wang lei^{1, 2
,
,},
Yu Xuan³,
Xu Lingyu³

1.
Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin 300171, China
2.
East Sea Information Center of State Oceanic Administration, Shanghai 200136, China
3.
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Received Date: 2019-03-12
Rev Recd Date: 2019-06-28

Available Online: 2020-11-18

Publish Date: 2020-02-25

Abstract

Abstract

Prediction of chlorophyll a concentration in traditional artificial network methods has some disadvantages, such as slower training speed, lower convergence precision, and easy to fall into local optimum situation. In particular, it is not possible to flexibly use historical information of any length to predict chlorophyll a concentration. To solve these problems, this paper defines the relationship between chlorophyll a concentration and various elements, depending on the long-term and short-term dependence between elements and the concentration of chlorophyll a. In this way, the long-term dependence between each element and the chlorophyll a concentration is separated from the short-term dependence. Then, based on the Long Short-Term Memory (LSTM), a merged LSTM prediction model was proposed. In this model, short and long term dependencies were presented respectively by different neurons and finally merged at the top of the model. The experimental data involves the continuous monitoring data of the station of Sandu Ao. The main result includes that the model has the advantage of fast reduction of training error, but also has significantly higher prediction accuracy compared with other three classical neural network models.
- chlorophyll a,
- Merged-LSTM,
- multi-factors,
- neural network

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References(23)

References

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